{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import tensorflow as tf\n",
    "import PIL.Image\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# www.kaggle.com/c/dogs-vs-cats/data\n",
    "Download dataset from link above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "original_dataset_dir = \"train\"      # jis folders mai sare pics hai name ke sath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_dir = 'cats_and_dogs_small' \n",
    "os.mkdir(base_dir)                          # created new empty folder in current directory\n",
    "train_dir = os.path.join(base_dir, 'train')\n",
    "os.mkdir(train_dir)                         # us folder ke ander train ka folder banaya\n",
    "validation_dir = os.path.join(base_dir, 'validation')\n",
    "os.mkdir(validation_dir)                    # and so on\n",
    "test_dir = os.path.join(base_dir, 'test')\n",
    "os.mkdir(test_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_cats_dir = os.path.join(train_dir, 'cats') \n",
    "os.mkdir(train_cats_dir)                   # cat ka folder banaya train ke ander\n",
    "train_dogs_dir = os.path.join(train_dir, 'dogs')\n",
    "os.mkdir(train_dogs_dir)                   # dog ka folder banaya train ke ander\n",
    "\n",
    "test_cats_dir = os.path.join(test_dir, 'cats')\n",
    "os.mkdir(test_cats_dir)                    # cat ka folder banaya test ke ander\n",
    "test_dogs_dir = os.path.join(test_dir, 'dogs')\n",
    "os.mkdir(test_dogs_dir)                    # dog ka folder banaya test ke ander\n",
    "\n",
    "validation_cats_dir = os.path.join(validation_dir, 'cats')\n",
    "os.mkdir(validation_cats_dir)               # cat ka folder banaya validation ke ander\n",
    "validation_dogs_dir = os.path.join(validation_dir, 'dogs')\n",
    "os.mkdir(validation_dogs_dir)               # dog ka folder banaya validation ke ander"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we only created empty folders in above coding\n",
    "# os.mkdir se new folder bante hai bake folder names ya path hai\n",
    "# Now we are going to copy files in those folders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "fnames = []\n",
    "for i in range(1000):              # created names, not files, same as in orignal files name\n",
    "    a = 'cat.{}.jpg'.format(i)\n",
    "    fnames.append(a)\n",
    "for x in fnames:                   # copying cats pics in empty train folder\n",
    "    src = os.path.join(original_dataset_dir, x)\n",
    "    dst = os.path.join(train_cats_dir, x)\n",
    "    shutil.copyfile(src, dst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "fnames = []\n",
    "for i in range(1000, 1500):              # created names, not files, same as in orignal files name\n",
    "    a = 'cat.{}.jpg'.format(i)\n",
    "    fnames.append(a)\n",
    "for x in fnames:                        # copying cats pics in empty validation folder\n",
    "    src = os.path.join(original_dataset_dir, x)\n",
    "    dst = os.path.join(validation_cats_dir, x)\n",
    "    shutil.copyfile(src, dst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "fnames = []\n",
    "for i in range(1500, 2000):              # created names, not files, same as in orignal files name\n",
    "    a = 'cat.{}.jpg'.format(i)\n",
    "    fnames.append(a)\n",
    "for x in fnames:                         # copying cats pics in empty test folder\n",
    "    src = os.path.join(original_dataset_dir, x)\n",
    "    dst = os.path.join(test_cats_dir, x)\n",
    "    shutil.copyfile(src, dst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# above copied 1000 train, 500 test $ 500 validation cats pictures\n",
    "# now doing same for dogs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "fnames = []\n",
    "for i in range(1000):              # created names, not files, same as in orignal files name\n",
    "    a = 'dog.{}.jpg'.format(i)\n",
    "    fnames.append(a)\n",
    "for x in fnames:                   # copying dogs pics in empty train folder\n",
    "    src = os.path.join(original_dataset_dir, x)\n",
    "    dst = os.path.join(train_dogs_dir, x)\n",
    "    shutil.copyfile(src, dst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "fnames = []\n",
    "for i in range(1000, 1500):              # created names, not files, same as in orignal files name\n",
    "    a = 'dog.{}.jpg'.format(i)\n",
    "    fnames.append(a)\n",
    "for x in fnames:                        # copying dogs pics in empty validation folder\n",
    "    src = os.path.join(original_dataset_dir, x)\n",
    "    dst = os.path.join(validation_dogs_dir, x)\n",
    "    shutil.copyfile(src, dst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "fnames = []\n",
    "for i in range(1500, 2000):              # created names, not files, same as in orignal files name\n",
    "    a = 'dog.{}.jpg'.format(i)\n",
    "    fnames.append(a)\n",
    "for x in fnames:                         # copying dogs pics in empty test folder\n",
    "    src = os.path.join(original_dataset_dir, x)\n",
    "    dst = os.path.join(test_dogs_dir, x)\n",
    "    shutil.copyfile(src, dst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# First model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 148, 148, 32)      896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 74, 74, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 72, 72, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 36, 36, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 34, 34, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 17, 17, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 15, 15, 128)       147584    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 6272)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 512)               3211776   \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 513       \n",
      "=================================================================\n",
      "Total params: 3,453,121\n",
      "Trainable params: 3,453,121\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "models = tf.keras.Sequential\n",
    "layers = tf.keras.layers\n",
    "\n",
    "model = models([layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)),\n",
    "                layers.MaxPooling2D((2, 2)),\n",
    "                layers.Conv2D(64, (3, 3), activation='relu'),\n",
    "                layers.MaxPooling2D((2, 2)),\n",
    "                layers.Conv2D(128, (3, 3), activation='relu'),\n",
    "                layers.MaxPooling2D((2, 2)),\n",
    "                layers.Conv2D(128, (3, 3), activation='relu'),\n",
    "                layers.MaxPooling2D((2, 2)),\n",
    "                layers.Flatten(),\n",
    "                layers.Dense(512, activation='relu'),\n",
    "                layers.Dense(1, activation='sigmoid')])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import optimizers\n",
    "\n",
    "model.compile(optimizer=optimizers.RMSprop(lr=1e-4),\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalizing Data\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "train_datagen = ImageDataGenerator(rescale=1./255)\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "# reshaping training images\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "                  train_dir,target_size=(150, 150),\n",
    "                  batch_size=20,class_mode='binary') # binary create labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "# reshaping validation images\n",
    "validation_generator = test_datagen.flow_from_directory(\n",
    "                       validation_dir,target_size=(150, 150),\n",
    "                       batch_size=20,class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "# reshaping training images\n",
    "test_generator =  train_datagen.flow_from_directory(\n",
    "                  test_dir,target_size=(150, 150),\n",
    "                  batch_size=20,class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "100/100 [==============================] - 19s 192ms/step - loss: 0.6912 - accuracy: 0.5230 - val_loss: 0.6699 - val_accuracy: 0.5950\n",
      "Epoch 2/30\n",
      "100/100 [==============================] - 16s 156ms/step - loss: 0.6607 - accuracy: 0.6010 - val_loss: 0.6427 - val_accuracy: 0.6220\n",
      "Epoch 3/30\n",
      "100/100 [==============================] - 16s 157ms/step - loss: 0.6264 - accuracy: 0.6550 - val_loss: 0.6200 - val_accuracy: 0.6520\n",
      "Epoch 4/30\n",
      "100/100 [==============================] - 16s 158ms/step - loss: 0.5813 - accuracy: 0.6975 - val_loss: 0.6197 - val_accuracy: 0.6350\n",
      "Epoch 5/30\n",
      "100/100 [==============================] - 16s 157ms/step - loss: 0.5400 - accuracy: 0.7350 - val_loss: 0.5752 - val_accuracy: 0.6910\n",
      "Epoch 6/30\n",
      "100/100 [==============================] - 16s 159ms/step - loss: 0.5007 - accuracy: 0.7495 - val_loss: 0.5718 - val_accuracy: 0.6960\n",
      "Epoch 7/30\n",
      "100/100 [==============================] - 16s 160ms/step - loss: 0.4790 - accuracy: 0.7700 - val_loss: 0.5554 - val_accuracy: 0.6980\n",
      "Epoch 8/30\n",
      "100/100 [==============================] - 16s 160ms/step - loss: 0.4514 - accuracy: 0.7935 - val_loss: 0.5353 - val_accuracy: 0.7240\n",
      "Epoch 9/30\n",
      "100/100 [==============================] - 16s 159ms/step - loss: 0.4157 - accuracy: 0.8070 - val_loss: 0.5293 - val_accuracy: 0.7290\n",
      "Epoch 10/30\n",
      "100/100 [==============================] - 16s 162ms/step - loss: 0.3895 - accuracy: 0.8225 - val_loss: 0.5288 - val_accuracy: 0.7310\n",
      "Epoch 11/30\n",
      "100/100 [==============================] - 16s 161ms/step - loss: 0.3612 - accuracy: 0.8430 - val_loss: 0.5468 - val_accuracy: 0.7360\n",
      "Epoch 12/30\n",
      "100/100 [==============================] - 16s 160ms/step - loss: 0.3369 - accuracy: 0.8605 - val_loss: 0.5207 - val_accuracy: 0.7620\n",
      "Epoch 13/30\n",
      "100/100 [==============================] - 16s 160ms/step - loss: 0.3068 - accuracy: 0.8815 - val_loss: 0.5345 - val_accuracy: 0.7510\n",
      "Epoch 14/30\n",
      "100/100 [==============================] - 16s 161ms/step - loss: 0.2905 - accuracy: 0.8835 - val_loss: 0.5796 - val_accuracy: 0.7490\n",
      "Epoch 15/30\n",
      "100/100 [==============================] - 16s 161ms/step - loss: 0.2693 - accuracy: 0.8910 - val_loss: 0.5667 - val_accuracy: 0.7500\n",
      "Epoch 16/30\n",
      "100/100 [==============================] - 16s 161ms/step - loss: 0.2508 - accuracy: 0.8995 - val_loss: 0.5735 - val_accuracy: 0.7510\n",
      "Epoch 17/30\n",
      "100/100 [==============================] - 16s 159ms/step - loss: 0.2252 - accuracy: 0.9145 - val_loss: 0.5706 - val_accuracy: 0.7550\n",
      "Epoch 18/30\n",
      "100/100 [==============================] - 16s 161ms/step - loss: 0.2070 - accuracy: 0.9225 - val_loss: 0.6782 - val_accuracy: 0.7280\n",
      "Epoch 19/30\n",
      "100/100 [==============================] - 16s 161ms/step - loss: 0.1824 - accuracy: 0.9330 - val_loss: 0.7391 - val_accuracy: 0.7090\n",
      "Epoch 20/30\n",
      "100/100 [==============================] - 16s 159ms/step - loss: 0.1663 - accuracy: 0.9385 - val_loss: 0.6212 - val_accuracy: 0.7410\n",
      "Epoch 21/30\n",
      "100/100 [==============================] - 28s 280ms/step - loss: 0.1450 - accuracy: 0.9535 - val_loss: 0.6565 - val_accuracy: 0.7490\n",
      "Epoch 22/30\n",
      "100/100 [==============================] - 31s 311ms/step - loss: 0.1253 - accuracy: 0.9635 - val_loss: 0.7705 - val_accuracy: 0.7450\n",
      "Epoch 23/30\n",
      "100/100 [==============================] - 16s 161ms/step - loss: 0.1091 - accuracy: 0.9670 - val_loss: 0.7283 - val_accuracy: 0.7490\n",
      "Epoch 24/30\n",
      "100/100 [==============================] - 16s 161ms/step - loss: 0.1004 - accuracy: 0.9660 - val_loss: 0.7601 - val_accuracy: 0.7500\n",
      "Epoch 25/30\n",
      "100/100 [==============================] - 16s 161ms/step - loss: 0.0790 - accuracy: 0.9800 - val_loss: 0.7865 - val_accuracy: 0.7350\n",
      "Epoch 26/30\n",
      "100/100 [==============================] - 16s 163ms/step - loss: 0.0711 - accuracy: 0.9780 - val_loss: 0.8762 - val_accuracy: 0.7390\n",
      "Epoch 27/30\n",
      "100/100 [==============================] - 19s 194ms/step - loss: 0.0563 - accuracy: 0.9840 - val_loss: 0.8407 - val_accuracy: 0.7450\n",
      "Epoch 28/30\n",
      "100/100 [==============================] - 33s 328ms/step - loss: 0.0540 - accuracy: 0.9835 - val_loss: 0.8943 - val_accuracy: 0.7520\n",
      "Epoch 29/30\n",
      "100/100 [==============================] - 33s 331ms/step - loss: 0.0483 - accuracy: 0.9865 - val_loss: 1.0264 - val_accuracy: 0.7310\n",
      "Epoch 30/30\n",
      "100/100 [==============================] - 27s 269ms/step - loss: 0.0389 - accuracy: 0.9920 - val_loss: 0.9434 - val_accuracy: 0.7610\n"
     ]
    }
   ],
   "source": [
    "epochs = 30\n",
    "history = model.fit_generator(\n",
    "          train_generator,steps_per_epoch=100,\n",
    "          epochs=epochs,validation_data=validation_generator,\n",
    "          validation_steps=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#evaluation = model.evaluate_generator(test_generator,steps=None,callbacks=None,max_queue_size=10,\n",
    "#                                      workers=1,use_multiprocessing=False,verbose=0,)\n",
    "#print(\"Loss: \",evaluation[0]*100,\"%\")\n",
    "#print(\"Accuracy: \",evaluation[1]*100,\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history.history.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting training and validation loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs = range(1, len(loss) + 1)\n",
    "\n",
    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting training and validation accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = history.history['accuracy']\n",
    "val_acc = history.history['val_accuracy']\n",
    "\n",
    "epochs = range(1, len(acc) + 1)\n",
    "\n",
    "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation acc')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('acc')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving the model\n",
    "model.save('cats_and_dogs_small_1.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using data augmentation to create new pictures\n",
    "### because we use very little data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "datagen = ImageDataGenerator(rotation_range=40,width_shift_range=0.2,\n",
    "                             height_shift_range=0.2,shear_range=0.2,\n",
    "                             zoom_range=0.2,horizontal_flip=True,fill_mode='nearest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Displaying some randomly augmented training images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cats_and_dogs_small\\train\\cats\\cat.100.jpg\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.preprocessing import image\n",
    "fnames = []\n",
    "for i in os.listdir(train_cats_dir):\n",
    "    a = os.path.join(train_cats_dir, i)\n",
    "    fnames.append(a)\n",
    "#fnames have the pictures names with location\n",
    "print(fnames[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=150x150 at 0x255A64D2F88>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets show 1 image\n",
    "img_path = fnames[3]\n",
    "img = image.load_img(img_path, target_size=(150, 150))\n",
    "img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# converted above picture in ndarray for data augmentaion\n",
    "x = image.img_to_array(img)\n",
    "x = x.reshape((1,) + x.shape)\n",
    "#x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# making 4 pictures from 1 picture\n",
    "i=0\n",
    "for batch in datagen.flow(x, batch_size=1):\n",
    "    plt.figure(i)\n",
    "    imgplot = plt.imshow(image.array_to_img(batch[0]))\n",
    "    i += 1\n",
    "    if i % 4 == 0:\n",
    "        break\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Defining a new convnet that includes dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_4 (Conv2D)            (None, 148, 148, 32)      896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 74, 74, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 72, 72, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 36, 36, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 34, 34, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 17, 17, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 15, 15, 128)       147584    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 6272)              0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 6272)              0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 512)               3211776   \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 513       \n",
      "=================================================================\n",
      "Total params: 3,453,121\n",
      "Trainable params: 3,453,121\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "models = tf.keras.Sequential\n",
    "layers = tf.keras.layers\n",
    "\n",
    "model1 = models([layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)),\n",
    "                layers.MaxPooling2D((2, 2)),\n",
    "                layers.Conv2D(64, (3, 3), activation='relu'),\n",
    "                layers.MaxPooling2D((2, 2)),\n",
    "                layers.Conv2D(128, (3, 3), activation='relu'),\n",
    "                layers.MaxPooling2D((2, 2)),\n",
    "                layers.Conv2D(128, (3, 3), activation='relu'),\n",
    "                layers.MaxPooling2D((2, 2)),\n",
    "                layers.Flatten(),\n",
    "                layers.Dropout(0.5),\n",
    "                layers.Dense(512, activation='relu'),\n",
    "                layers.Dense(1, activation='sigmoid')])\n",
    "\n",
    "model1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import optimizers\n",
    "\n",
    "model1.compile(optimizer=optimizers.RMSprop(lr=1e-4),\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training the convnet using data-augmentation generators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# genrating new pictures and rescaling it\n",
    "\n",
    "train_datagen = ImageDataGenerator(rescale=1./255,rotation_range=40,\n",
    "                width_shift_range=0.2,height_shift_range=0.2,\n",
    "                shear_range=0.2,zoom_range=0.2,horizontal_flip=True,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# only rescaling\n",
    "\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note that the validation data shouldn’t be augmented!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_generator = train_datagen.flow_from_directory(\n",
    "                  train_dir,target_size=(150, 150),\n",
    "                  batch_size=20,class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "validation_generator = test_datagen.flow_from_directory(\n",
    "                       validation_dir,target_size=(150, 150),\n",
    "                       batch_size=20,class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "100/100 [==============================] - 25s 246ms/step - loss: 0.6949 - accuracy: 0.5015 - val_loss: 0.6883 - val_accuracy: 0.5950\n",
      "Epoch 2/100\n",
      "100/100 [==============================] - 22s 224ms/step - loss: 0.6883 - accuracy: 0.5455 - val_loss: 0.6923 - val_accuracy: 0.5010\n",
      "Epoch 3/100\n",
      "100/100 [==============================] - 22s 222ms/step - loss: 0.6754 - accuracy: 0.5710 - val_loss: 0.6873 - val_accuracy: 0.5330\n",
      "Epoch 4/100\n",
      "100/100 [==============================] - 22s 223ms/step - loss: 0.6611 - accuracy: 0.6000 - val_loss: 0.6328 - val_accuracy: 0.6350\n",
      "Epoch 5/100\n",
      "100/100 [==============================] - 52s 523ms/step - loss: 0.6452 - accuracy: 0.6160 - val_loss: 0.6299 - val_accuracy: 0.6300\n",
      "Epoch 6/100\n",
      "100/100 [==============================] - 22s 223ms/step - loss: 0.6310 - accuracy: 0.6525 - val_loss: 0.6785 - val_accuracy: 0.5810\n",
      "Epoch 7/100\n",
      "100/100 [==============================] - 22s 223ms/step - loss: 0.6158 - accuracy: 0.6575 - val_loss: 0.5871 - val_accuracy: 0.6870\n",
      "Epoch 8/100\n",
      "100/100 [==============================] - 22s 223ms/step - loss: 0.6066 - accuracy: 0.6555 - val_loss: 0.5959 - val_accuracy: 0.6720\n",
      "Epoch 9/100\n",
      "100/100 [==============================] - 52s 520ms/step - loss: 0.6039 - accuracy: 0.6805 - val_loss: 0.5985 - val_accuracy: 0.6680\n",
      "Epoch 10/100\n",
      "100/100 [==============================] - 23s 225ms/step - loss: 0.5947 - accuracy: 0.6815 - val_loss: 0.5826 - val_accuracy: 0.6710\n",
      "Epoch 11/100\n",
      "100/100 [==============================] - 22s 224ms/step - loss: 0.5893 - accuracy: 0.6800 - val_loss: 0.6155 - val_accuracy: 0.6730\n",
      "Epoch 12/100\n",
      "100/100 [==============================] - 22s 222ms/step - loss: 0.5873 - accuracy: 0.6900 - val_loss: 0.5508 - val_accuracy: 0.7060\n",
      "Epoch 13/100\n",
      "100/100 [==============================] - 57s 566ms/step - loss: 0.5717 - accuracy: 0.6965 - val_loss: 0.5916 - val_accuracy: 0.6790\n",
      "Epoch 14/100\n",
      "100/100 [==============================] - 22s 225ms/step - loss: 0.5691 - accuracy: 0.6980 - val_loss: 0.5591 - val_accuracy: 0.6960\n",
      "Epoch 15/100\n",
      "100/100 [==============================] - 22s 222ms/step - loss: 0.5769 - accuracy: 0.6965 - val_loss: 0.5888 - val_accuracy: 0.6780\n",
      "Epoch 16/100\n",
      "100/100 [==============================] - 22s 223ms/step - loss: 0.5775 - accuracy: 0.6825 - val_loss: 0.5527 - val_accuracy: 0.7010\n",
      "Epoch 17/100\n",
      "100/100 [==============================] - 60s 604ms/step - loss: 0.5686 - accuracy: 0.7175 - val_loss: 0.5332 - val_accuracy: 0.7320\n",
      "Epoch 18/100\n",
      "100/100 [==============================] - 22s 224ms/step - loss: 0.5564 - accuracy: 0.7020 - val_loss: 0.5516 - val_accuracy: 0.7160\n",
      "Epoch 19/100\n",
      "100/100 [==============================] - 22s 224ms/step - loss: 0.5473 - accuracy: 0.7235 - val_loss: 0.5758 - val_accuracy: 0.6960\n",
      "Epoch 20/100\n",
      "100/100 [==============================] - 22s 225ms/step - loss: 0.5449 - accuracy: 0.7310 - val_loss: 0.5220 - val_accuracy: 0.7310\n",
      "Epoch 21/100\n",
      "100/100 [==============================] - 56s 556ms/step - loss: 0.5502 - accuracy: 0.7140 - val_loss: 0.5699 - val_accuracy: 0.7080\n",
      "Epoch 22/100\n",
      "100/100 [==============================] - 22s 224ms/step - loss: 0.5383 - accuracy: 0.7375 - val_loss: 0.5130 - val_accuracy: 0.7400\n",
      "Epoch 23/100\n",
      "100/100 [==============================] - 22s 223ms/step - loss: 0.5471 - accuracy: 0.7200 - val_loss: 0.5188 - val_accuracy: 0.7350\n",
      "Epoch 24/100\n",
      "100/100 [==============================] - 28s 277ms/step - loss: 0.5304 - accuracy: 0.7330 - val_loss: 0.5464 - val_accuracy: 0.7250\n",
      "Epoch 25/100\n",
      "100/100 [==============================] - 50s 501ms/step - loss: 0.5297 - accuracy: 0.7295 - val_loss: 0.5011 - val_accuracy: 0.7520\n",
      "Epoch 26/100\n",
      "100/100 [==============================] - 22s 225ms/step - loss: 0.5300 - accuracy: 0.7320 - val_loss: 0.5581 - val_accuracy: 0.7080\n",
      "Epoch 27/100\n",
      "100/100 [==============================] - 23s 225ms/step - loss: 0.5312 - accuracy: 0.7315 - val_loss: 0.5308 - val_accuracy: 0.7330\n",
      "Epoch 28/100\n",
      "100/100 [==============================] - 26s 255ms/step - loss: 0.5247 - accuracy: 0.7335 - val_loss: 0.5335 - val_accuracy: 0.7380\n",
      "Epoch 29/100\n",
      "100/100 [==============================] - 55s 554ms/step - loss: 0.5188 - accuracy: 0.7440 - val_loss: 0.5418 - val_accuracy: 0.7230\n",
      "Epoch 30/100\n",
      "100/100 [==============================] - 22s 222ms/step - loss: 0.5154 - accuracy: 0.7410 - val_loss: 0.5230 - val_accuracy: 0.7370\n",
      "Epoch 31/100\n",
      "100/100 [==============================] - 22s 222ms/step - loss: 0.5112 - accuracy: 0.7535 - val_loss: 0.5157 - val_accuracy: 0.7440\n",
      "Epoch 32/100\n",
      "100/100 [==============================] - 27s 269ms/step - loss: 0.5069 - accuracy: 0.7500 - val_loss: 0.5007 - val_accuracy: 0.7510\n",
      "Epoch 33/100\n",
      "100/100 [==============================] - 54s 538ms/step - loss: 0.5219 - accuracy: 0.7465 - val_loss: 0.5633 - val_accuracy: 0.7170\n",
      "Epoch 34/100\n",
      "100/100 [==============================] - 23s 225ms/step - loss: 0.5129 - accuracy: 0.7525 - val_loss: 0.5289 - val_accuracy: 0.7290\n",
      "Epoch 35/100\n",
      "100/100 [==============================] - 22s 224ms/step - loss: 0.4975 - accuracy: 0.7525 - val_loss: 0.4945 - val_accuracy: 0.7560\n",
      "Epoch 36/100\n",
      "100/100 [==============================] - 27s 266ms/step - loss: 0.5123 - accuracy: 0.7490 - val_loss: 0.4836 - val_accuracy: 0.7600\n",
      "Epoch 37/100\n",
      "100/100 [==============================] - 57s 575ms/step - loss: 0.4911 - accuracy: 0.7590 - val_loss: 0.4984 - val_accuracy: 0.7610\n",
      "Epoch 38/100\n",
      "100/100 [==============================] - 22s 224ms/step - loss: 0.4957 - accuracy: 0.7610 - val_loss: 0.4831 - val_accuracy: 0.7570\n",
      "Epoch 39/100\n",
      "100/100 [==============================] - 22s 225ms/step - loss: 0.4956 - accuracy: 0.7520 - val_loss: 0.5065 - val_accuracy: 0.7430\n",
      "Epoch 40/100\n",
      "100/100 [==============================] - 26s 260ms/step - loss: 0.4873 - accuracy: 0.7620 - val_loss: 0.4952 - val_accuracy: 0.7610\n",
      "Epoch 41/100\n",
      "100/100 [==============================] - 58s 580ms/step - loss: 0.4943 - accuracy: 0.7595 - val_loss: 0.4750 - val_accuracy: 0.7600\n",
      "Epoch 42/100\n",
      "100/100 [==============================] - 22s 222ms/step - loss: 0.4893 - accuracy: 0.7640 - val_loss: 0.4905 - val_accuracy: 0.7570\n",
      "Epoch 43/100\n",
      "100/100 [==============================] - 22s 224ms/step - loss: 0.4862 - accuracy: 0.7730 - val_loss: 0.4904 - val_accuracy: 0.7630\n",
      "Epoch 44/100\n",
      "100/100 [==============================] - 28s 277ms/step - loss: 0.4734 - accuracy: 0.7710 - val_loss: 0.4706 - val_accuracy: 0.7720\n",
      "Epoch 45/100\n",
      "100/100 [==============================] - 53s 534ms/step - loss: 0.4880 - accuracy: 0.7560 - val_loss: 0.5051 - val_accuracy: 0.7420\n",
      "Epoch 46/100\n",
      "100/100 [==============================] - 22s 224ms/step - loss: 0.4781 - accuracy: 0.7710 - val_loss: 0.4797 - val_accuracy: 0.7700\n",
      "Epoch 47/100\n",
      "100/100 [==============================] - 23s 226ms/step - loss: 0.4707 - accuracy: 0.7740 - val_loss: 0.5296 - val_accuracy: 0.7520\n",
      "Epoch 48/100\n",
      "100/100 [==============================] - 28s 277ms/step - loss: 0.4697 - accuracy: 0.7710 - val_loss: 0.4632 - val_accuracy: 0.7840\n",
      "Epoch 49/100\n",
      "100/100 [==============================] - 57s 572ms/step - loss: 0.4657 - accuracy: 0.7700 - val_loss: 0.4769 - val_accuracy: 0.7640\n",
      "Epoch 50/100\n",
      "100/100 [==============================] - 22s 222ms/step - loss: 0.4696 - accuracy: 0.7665 - val_loss: 0.4839 - val_accuracy: 0.7630\n",
      "Epoch 51/100\n",
      "100/100 [==============================] - 22s 222ms/step - loss: 0.4712 - accuracy: 0.7670 - val_loss: 0.4590 - val_accuracy: 0.7760\n",
      "Epoch 52/100\n",
      "100/100 [==============================] - 28s 275ms/step - loss: 0.4683 - accuracy: 0.7830 - val_loss: 0.4400 - val_accuracy: 0.7850\n",
      "Epoch 53/100\n",
      "100/100 [==============================] - 57s 571ms/step - loss: 0.4538 - accuracy: 0.7810 - val_loss: 0.4549 - val_accuracy: 0.7870\n",
      "Epoch 54/100\n",
      "100/100 [==============================] - 22s 223ms/step - loss: 0.4652 - accuracy: 0.7800 - val_loss: 0.4721 - val_accuracy: 0.7750\n",
      "Epoch 55/100\n",
      "100/100 [==============================] - 22s 223ms/step - loss: 0.4687 - accuracy: 0.7810 - val_loss: 0.4617 - val_accuracy: 0.7800\n",
      "Epoch 56/100\n",
      "100/100 [==============================] - 26s 255ms/step - loss: 0.4758 - accuracy: 0.7805 - val_loss: 0.4590 - val_accuracy: 0.7820\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 57/100\n",
      "100/100 [==============================] - 57s 571ms/step - loss: 0.4598 - accuracy: 0.7905 - val_loss: 0.4679 - val_accuracy: 0.7750\n",
      "Epoch 58/100\n",
      "100/100 [==============================] - 22s 216ms/step - loss: 0.4646 - accuracy: 0.7830 - val_loss: 0.4723 - val_accuracy: 0.7770\n",
      "Epoch 59/100\n",
      "100/100 [==============================] - 22s 218ms/step - loss: 0.4592 - accuracy: 0.7880 - val_loss: 0.4594 - val_accuracy: 0.7810\n",
      "Epoch 60/100\n",
      "100/100 [==============================] - 27s 265ms/step - loss: 0.4458 - accuracy: 0.7840 - val_loss: 0.4859 - val_accuracy: 0.7660\n",
      "Epoch 61/100\n",
      "100/100 [==============================] - 59s 595ms/step - loss: 0.4389 - accuracy: 0.7955 - val_loss: 0.4421 - val_accuracy: 0.7940\n",
      "Epoch 62/100\n",
      "100/100 [==============================] - 22s 218ms/step - loss: 0.4411 - accuracy: 0.7905 - val_loss: 0.4772 - val_accuracy: 0.7670\n",
      "Epoch 63/100\n",
      "100/100 [==============================] - 22s 216ms/step - loss: 0.4447 - accuracy: 0.7795 - val_loss: 0.5300 - val_accuracy: 0.7460\n",
      "Epoch 64/100\n",
      "100/100 [==============================] - 22s 216ms/step - loss: 0.4447 - accuracy: 0.7845 - val_loss: 0.4456 - val_accuracy: 0.7930\n",
      "Epoch 65/100\n",
      "100/100 [==============================] - 56s 564ms/step - loss: 0.4393 - accuracy: 0.7960 - val_loss: 0.4927 - val_accuracy: 0.7780\n",
      "Epoch 66/100\n",
      "100/100 [==============================] - 22s 217ms/step - loss: 0.4380 - accuracy: 0.7935 - val_loss: 0.4731 - val_accuracy: 0.7780\n",
      "Epoch 67/100\n",
      "100/100 [==============================] - 21s 215ms/step - loss: 0.4429 - accuracy: 0.7820 - val_loss: 0.5427 - val_accuracy: 0.7460\n",
      "Epoch 68/100\n",
      "100/100 [==============================] - 25s 253ms/step - loss: 0.4439 - accuracy: 0.7825 - val_loss: 0.4379 - val_accuracy: 0.7940\n",
      "Epoch 69/100\n",
      "100/100 [==============================] - 54s 538ms/step - loss: 0.4418 - accuracy: 0.7900 - val_loss: 0.4994 - val_accuracy: 0.7650\n",
      "Epoch 70/100\n",
      "100/100 [==============================] - 22s 218ms/step - loss: 0.4350 - accuracy: 0.7940 - val_loss: 0.4408 - val_accuracy: 0.8050\n",
      "Epoch 71/100\n",
      "100/100 [==============================] - 22s 215ms/step - loss: 0.4316 - accuracy: 0.7975 - val_loss: 0.4359 - val_accuracy: 0.8000\n",
      "Epoch 72/100\n",
      "100/100 [==============================] - 22s 217ms/step - loss: 0.4303 - accuracy: 0.8015 - val_loss: 0.4766 - val_accuracy: 0.7770\n",
      "Epoch 73/100\n",
      "100/100 [==============================] - 56s 563ms/step - loss: 0.4276 - accuracy: 0.7980 - val_loss: 0.4570 - val_accuracy: 0.8020\n",
      "Epoch 74/100\n",
      "100/100 [==============================] - 22s 218ms/step - loss: 0.4185 - accuracy: 0.8065 - val_loss: 0.4768 - val_accuracy: 0.7940\n",
      "Epoch 75/100\n",
      "100/100 [==============================] - 22s 217ms/step - loss: 0.4241 - accuracy: 0.8090 - val_loss: 0.4682 - val_accuracy: 0.8000\n",
      "Epoch 76/100\n",
      "100/100 [==============================] - 25s 247ms/step - loss: 0.4199 - accuracy: 0.8030 - val_loss: 0.4398 - val_accuracy: 0.7930\n",
      "Epoch 77/100\n",
      "100/100 [==============================] - 55s 552ms/step - loss: 0.4189 - accuracy: 0.8125 - val_loss: 0.4499 - val_accuracy: 0.7950\n",
      "Epoch 78/100\n",
      "100/100 [==============================] - 22s 218ms/step - loss: 0.4190 - accuracy: 0.8030 - val_loss: 0.4413 - val_accuracy: 0.8070\n",
      "Epoch 79/100\n",
      "100/100 [==============================] - 22s 216ms/step - loss: 0.4248 - accuracy: 0.8025 - val_loss: 0.4892 - val_accuracy: 0.7850\n",
      "Epoch 80/100\n",
      "100/100 [==============================] - 25s 253ms/step - loss: 0.4315 - accuracy: 0.7995 - val_loss: 0.4373 - val_accuracy: 0.7970\n",
      "Epoch 81/100\n",
      "100/100 [==============================] - 52s 521ms/step - loss: 0.4109 - accuracy: 0.8085 - val_loss: 0.4200 - val_accuracy: 0.8140\n",
      "Epoch 82/100\n",
      "100/100 [==============================] - 22s 217ms/step - loss: 0.4076 - accuracy: 0.8190 - val_loss: 0.4006 - val_accuracy: 0.8270\n",
      "Epoch 83/100\n",
      "100/100 [==============================] - 22s 218ms/step - loss: 0.4117 - accuracy: 0.8145 - val_loss: 0.5254 - val_accuracy: 0.7740\n",
      "Epoch 84/100\n",
      "100/100 [==============================] - 25s 253ms/step - loss: 0.4204 - accuracy: 0.8115 - val_loss: 0.4177 - val_accuracy: 0.8200\n",
      "Epoch 85/100\n",
      "100/100 [==============================] - 60s 600ms/step - loss: 0.4140 - accuracy: 0.8075 - val_loss: 0.4589 - val_accuracy: 0.8000\n",
      "Epoch 86/100\n",
      "100/100 [==============================] - 22s 217ms/step - loss: 0.3870 - accuracy: 0.8310 - val_loss: 0.4404 - val_accuracy: 0.8100\n",
      "Epoch 87/100\n",
      "100/100 [==============================] - 22s 218ms/step - loss: 0.4229 - accuracy: 0.8125 - val_loss: 0.4326 - val_accuracy: 0.8050\n",
      "Epoch 88/100\n",
      "100/100 [==============================] - 25s 255ms/step - loss: 0.4142 - accuracy: 0.8090 - val_loss: 0.4399 - val_accuracy: 0.7980\n",
      "Epoch 89/100\n",
      "100/100 [==============================] - 59s 588ms/step - loss: 0.3888 - accuracy: 0.8260 - val_loss: 0.4360 - val_accuracy: 0.8090\n",
      "Epoch 90/100\n",
      "100/100 [==============================] - 22s 217ms/step - loss: 0.4094 - accuracy: 0.8180 - val_loss: 0.4286 - val_accuracy: 0.8070\n",
      "Epoch 91/100\n",
      "100/100 [==============================] - 22s 217ms/step - loss: 0.3958 - accuracy: 0.8130 - val_loss: 0.4735 - val_accuracy: 0.8190\n",
      "Epoch 92/100\n",
      "100/100 [==============================] - 22s 216ms/step - loss: 0.3897 - accuracy: 0.8250 - val_loss: 0.4010 - val_accuracy: 0.8230\n",
      "Epoch 93/100\n",
      "100/100 [==============================] - 55s 548ms/step - loss: 0.3908 - accuracy: 0.8370 - val_loss: 0.4912 - val_accuracy: 0.7740\n",
      "Epoch 94/100\n",
      "100/100 [==============================] - 22s 217ms/step - loss: 0.3858 - accuracy: 0.8325 - val_loss: 0.4072 - val_accuracy: 0.8210\n",
      "Epoch 95/100\n",
      "100/100 [==============================] - 22s 218ms/step - loss: 0.3937 - accuracy: 0.8250 - val_loss: 0.4511 - val_accuracy: 0.8070\n",
      "Epoch 96/100\n",
      "100/100 [==============================] - 26s 263ms/step - loss: 0.3863 - accuracy: 0.8250 - val_loss: 0.4406 - val_accuracy: 0.8030\n",
      "Epoch 97/100\n",
      "100/100 [==============================] - 56s 556ms/step - loss: 0.4085 - accuracy: 0.8190 - val_loss: 0.4227 - val_accuracy: 0.8140\n",
      "Epoch 98/100\n",
      "100/100 [==============================] - 22s 218ms/step - loss: 0.3880 - accuracy: 0.8235 - val_loss: 0.4440 - val_accuracy: 0.7920\n",
      "Epoch 99/100\n",
      "100/100 [==============================] - 21s 215ms/step - loss: 0.3993 - accuracy: 0.8190 - val_loss: 0.4190 - val_accuracy: 0.8130\n",
      "Epoch 100/100\n",
      "100/100 [==============================] - 26s 255ms/step - loss: 0.3822 - accuracy: 0.8230 - val_loss: 0.4167 - val_accuracy: 0.8150\n"
     ]
    }
   ],
   "source": [
    "epochs = 100\n",
    "history1 = model1.fit_generator(\n",
    "          train_generator,steps_per_epoch=100,\n",
    "          epochs=epochs,validation_data=validation_generator,\n",
    "          validation_steps=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#evaluation1 = model1.evaluate_generator(test_generator,steps=None,callbacks=None,max_queue_size=10,\n",
    "#                                      workers=1,use_multiprocessing=False,verbose=0,)\n",
    "#print(\"Loss: \",evaluation1[0]*100,\"%\")\n",
    "#print(\"Accuracy: \",evaluation1[1]*100,\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history1.history.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting training and validation loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = history1.history['loss']\n",
    "val_loss = history1.history['val_loss']\n",
    "\n",
    "epochs = range(1, len(loss) + 1)\n",
    "\n",
    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting training and validation Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = history1.history['accuracy']\n",
    "val_acc = history1.history['val_accuracy']\n",
    "\n",
    "epochs = range(1, len(acc) + 1)\n",
    "\n",
    "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation acc')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('acc')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving model\n",
    "model1.save('cats_and_dogs_small_2.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature extraction without DATA AUGMENTATION\n",
    "  pretrained models mention in book Deep learning with Python by (François Chollet) (page no:145)\n",
    "  \n",
    "     Xception\n",
    "     Inception V3\n",
    "     ResNet50\n",
    "     VGG16\n",
    "     VGG19\n",
    "     MobileNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we will use VGG16 model and extract features from it to use it on our data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Description of VGG16 model\n",
    "VGG16 (also called OxfordNet) is a convolutional neural network architecture named after the Visual Geometry Group from Oxford, who developed it.\n",
    "It was used to win the ILSVR (ImageNet) competition in 2014.\n",
    "To this day is it still considered to be an excellent vision model, although it has been somewhat outperformed by more revent advances such as Inception and ResNet.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np \n",
    "import shutil\n",
    "import tensorflow as tf\n",
    "import PIL.Image\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cats_and_dogs_small\\train\n",
      "cats_and_dogs_small\\validation\n",
      "cats_and_dogs_small\\test\n"
     ]
    }
   ],
   "source": [
    "# location of files\n",
    "# using files that we copied in the start\n",
    "\n",
    "original_dataset_dir = \"train\"\n",
    "base_dir = 'cats_and_dogs_small'\n",
    "train_dir = os.path.join(base_dir, 'train')\n",
    "validation_dir = os.path.join(base_dir, 'validation')\n",
    "test_dir = os.path.join(base_dir, 'test')\n",
    "print(train_dir)\n",
    "print(validation_dir)\n",
    "print(test_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loaded the model\n",
    "from tensorflow.keras.applications import VGG16\n",
    "conv_base = VGG16(weights='imagenet',include_top=False, # refers to including (or not) the densely connected classifier\n",
    "                  input_shape=(150, 150, 3))     # optional: if you don’t pass it,be able to process inputs of any size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"vgg16\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_2 (InputLayer)         [(None, 150, 150, 3)]     0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 150, 150, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 150, 150, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 75, 75, 64)        0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 75, 75, 128)       73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 75, 75, 128)       147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 37, 37, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 37, 37, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 37, 37, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 37, 37, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 18, 18, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 18, 18, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 18, 18, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 18, 18, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 9, 9, 512)         0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 4, 4, 512)         0         \n",
      "=================================================================\n",
      "Total params: 14,714,688\n",
      "Trainable params: 14,714,688\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "conv_base.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "datagen = ImageDataGenerator(rescale=1./255)\n",
    "batch_size = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_features(directory, sample_count):\n",
    "    features = np.zeros(shape=(sample_count, 4, 4, 512))\n",
    "    labels = np.zeros(shape=(sample_count))\n",
    "    generator = datagen.flow_from_directory(\n",
    "                    directory,\n",
    "                    target_size=(150, 150),\n",
    "                    batch_size=batch_size,\n",
    "                    class_mode='binary')\n",
    "    \n",
    "    i = 0\n",
    "    for inputs_batch, labels_batch in generator:\n",
    "        features_batch = conv_base.predict(inputs_batch)\n",
    "        features[i * batch_size : (i + 1) * batch_size] = features_batch\n",
    "        labels[i * batch_size : (i + 1) * batch_size] = labels_batch\n",
    "        i += 1\n",
    "        if i * batch_size >= sample_count:\n",
    "            break\n",
    "    return features, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2000 images belonging to 2 classes.\n",
      "Found 1000 images belonging to 2 classes.\n",
      "Found 1000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_features, train_labels = extract_features(train_dir, 2000)\n",
    "validation_features, validation_labels = extract_features(validation_dir, 1000)\n",
    "test_features, test_labels = extract_features(test_dir, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_features = np.reshape(train_features, (2000, 4*4* 512))\n",
    "validation_features = np.reshape(validation_features, (1000, 4*4* 512))\n",
    "test_features = np.reshape(test_features, (1000, 4*4* 512))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Defining and training the densely connected classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 2000 samples, validate on 1000 samples\n",
      "Epoch 1/30\n",
      "2000/2000 [==============================] - 2s 1ms/sample - loss: 0.6407 - acc: 0.6350 - val_loss: 0.4467 - val_acc: 0.8340\n",
      "Epoch 2/30\n",
      "2000/2000 [==============================] - 1s 422us/sample - loss: 0.4443 - acc: 0.7985 - val_loss: 0.3585 - val_acc: 0.8750\n",
      "Epoch 3/30\n",
      "2000/2000 [==============================] - 1s 441us/sample - loss: 0.3663 - acc: 0.8390 - val_loss: 0.3176 - val_acc: 0.8820\n",
      "Epoch 4/30\n",
      "2000/2000 [==============================] - 1s 422us/sample - loss: 0.3175 - acc: 0.8630 - val_loss: 0.2937 - val_acc: 0.8930\n",
      "Epoch 5/30\n",
      "2000/2000 [==============================] - 1s 464us/sample - loss: 0.2813 - acc: 0.8950 - val_loss: 0.2783 - val_acc: 0.8930\n",
      "Epoch 6/30\n",
      "2000/2000 [==============================] - 1s 447us/sample - loss: 0.2683 - acc: 0.8965 - val_loss: 0.2684 - val_acc: 0.8960\n",
      "Epoch 7/30\n",
      "2000/2000 [==============================] - 1s 464us/sample - loss: 0.2507 - acc: 0.9085 - val_loss: 0.2605 - val_acc: 0.8990\n",
      "Epoch 8/30\n",
      "2000/2000 [==============================] - 1s 454us/sample - loss: 0.2346 - acc: 0.9055 - val_loss: 0.2559 - val_acc: 0.9020\n",
      "Epoch 9/30\n",
      "2000/2000 [==============================] - 1s 479us/sample - loss: 0.2227 - acc: 0.9165 - val_loss: 0.2518 - val_acc: 0.8990\n",
      "Epoch 10/30\n",
      "2000/2000 [==============================] - 1s 431us/sample - loss: 0.2076 - acc: 0.9245 - val_loss: 0.2623 - val_acc: 0.8920\n",
      "Epoch 11/30\n",
      "2000/2000 [==============================] - 1s 426us/sample - loss: 0.1957 - acc: 0.9220 - val_loss: 0.2476 - val_acc: 0.9010\n",
      "Epoch 12/30\n",
      "2000/2000 [==============================] - 1s 433us/sample - loss: 0.1832 - acc: 0.9340 - val_loss: 0.2392 - val_acc: 0.9050\n",
      "Epoch 13/30\n",
      "2000/2000 [==============================] - 1s 428us/sample - loss: 0.1805 - acc: 0.9375 - val_loss: 0.2375 - val_acc: 0.9030\n",
      "Epoch 14/30\n",
      "2000/2000 [==============================] - 1s 435us/sample - loss: 0.1673 - acc: 0.9420 - val_loss: 0.2355 - val_acc: 0.9030\n",
      "Epoch 15/30\n",
      "2000/2000 [==============================] - 1s 429us/sample - loss: 0.1587 - acc: 0.9465 - val_loss: 0.2359 - val_acc: 0.9050\n",
      "Epoch 16/30\n",
      "2000/2000 [==============================] - 1s 426us/sample - loss: 0.1519 - acc: 0.9470 - val_loss: 0.2350 - val_acc: 0.9040\n",
      "Epoch 17/30\n",
      "2000/2000 [==============================] - 1s 431us/sample - loss: 0.1508 - acc: 0.9490 - val_loss: 0.2425 - val_acc: 0.9040\n",
      "Epoch 18/30\n",
      "2000/2000 [==============================] - 1s 431us/sample - loss: 0.1457 - acc: 0.9485 - val_loss: 0.2349 - val_acc: 0.9050\n",
      "Epoch 19/30\n",
      "2000/2000 [==============================] - 1s 444us/sample - loss: 0.1317 - acc: 0.9560 - val_loss: 0.2357 - val_acc: 0.9080\n",
      "Epoch 20/30\n",
      "2000/2000 [==============================] - 1s 450us/sample - loss: 0.1363 - acc: 0.9520 - val_loss: 0.2354 - val_acc: 0.9020\n",
      "Epoch 21/30\n",
      "2000/2000 [==============================] - 1s 429us/sample - loss: 0.1212 - acc: 0.9600 - val_loss: 0.2344 - val_acc: 0.9060\n",
      "Epoch 22/30\n",
      "2000/2000 [==============================] - 1s 430us/sample - loss: 0.1208 - acc: 0.9560 - val_loss: 0.2614 - val_acc: 0.8910\n",
      "Epoch 23/30\n",
      "2000/2000 [==============================] - 1s 430us/sample - loss: 0.1161 - acc: 0.9565 - val_loss: 0.2348 - val_acc: 0.9010\n",
      "Epoch 24/30\n",
      "2000/2000 [==============================] - 1s 428us/sample - loss: 0.1117 - acc: 0.9595 - val_loss: 0.2351 - val_acc: 0.9020\n",
      "Epoch 25/30\n",
      "2000/2000 [==============================] - 1s 429us/sample - loss: 0.1016 - acc: 0.9670 - val_loss: 0.2490 - val_acc: 0.9050\n",
      "Epoch 26/30\n",
      "2000/2000 [==============================] - 1s 429us/sample - loss: 0.0991 - acc: 0.9710 - val_loss: 0.2361 - val_acc: 0.9050\n",
      "Epoch 27/30\n",
      "2000/2000 [==============================] - 1s 426us/sample - loss: 0.0969 - acc: 0.9695 - val_loss: 0.2397 - val_acc: 0.9030\n",
      "Epoch 28/30\n",
      "2000/2000 [==============================] - 1s 430us/sample - loss: 0.0918 - acc: 0.9735 - val_loss: 0.2430 - val_acc: 0.9060\n",
      "Epoch 29/30\n",
      "2000/2000 [==============================] - 1s 428us/sample - loss: 0.0877 - acc: 0.9750 - val_loss: 0.2475 - val_acc: 0.9070\n",
      "Epoch 30/30\n",
      "2000/2000 [==============================] - 1s 435us/sample - loss: 0.0835 - acc: 0.9760 - val_loss: 0.2421 - val_acc: 0.9010\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras import models\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras import optimizers\n",
    "\n",
    "model = models.Sequential()\n",
    "model.add(layers.Dense(256, activation='relu', input_dim=4 * 4 * 512))\n",
    "model.add(layers.Dropout(0.5))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(optimizer=optimizers.RMSprop(lr=2e-5),\n",
    "                loss='binary_crossentropy',\n",
    "                metrics=['acc'])\n",
    "\n",
    "history = model.fit(train_features, train_labels,\n",
    "                    epochs=30,\n",
    "                    batch_size=20,\n",
    "                    validation_data=(validation_features, validation_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "epochs = range(1, len(acc) + 1)\n",
    "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "plt.figure()\n",
    "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FEATURE EXTRACTION WITH DATA AUGMENTATION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding a densely connected classifier on top of the convolutional base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.applications import VGG16\n",
    "conv_base = VGG16(weights='imagenet',\n",
    "                  include_top=False,\n",
    "                  input_shape=(150, 150, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "vgg16 (Model)                (None, 4, 4, 512)         14714688  \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 8192)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 256)               2097408   \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 16,812,353\n",
      "Trainable params: 16,812,353\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras import models\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "model = models.Sequential()\n",
    "model.add(conv_base)\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(256, activation='relu'))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# freezing the convolutional base"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Keras, you freeze a network by setting its trainable attribute to False:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the number of trainable weights before freezing the conv base: 30\n"
     ]
    }
   ],
   "source": [
    "print('This is the number of trainable weights ''before freezing the conv base:', len(model.trainable_weights))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_base.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the number of trainable weights after freezing the conv base: 4\n"
     ]
    }
   ],
   "source": [
    " print('This is the number of trainable weights ''after freezing the conv base:', len(model.trainable_weights))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training the model end to end with a frozen convolutional base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cats_and_dogs_small\\train\n",
      "cats_and_dogs_small\\validation\n",
      "cats_and_dogs_small\\test\n"
     ]
    }
   ],
   "source": [
    "# location of files\n",
    "# using files that we copied in the start\n",
    "import os\n",
    "\n",
    "original_dataset_dir = \"train\"\n",
    "base_dir = 'cats_and_dogs_small'\n",
    "train_dir = os.path.join(base_dir, 'train')\n",
    "validation_dir = os.path.join(base_dir, 'validation')\n",
    "test_dir = os.path.join(base_dir, 'test')\n",
    "print(train_dir)\n",
    "print(validation_dir)\n",
    "print(test_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras import optimizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_datagen = ImageDataGenerator(\n",
    "                rescale=1./255,\n",
    "                rotation_range=40,\n",
    "                width_shift_range=0.2,\n",
    "                height_shift_range=0.2,\n",
    "                shear_range=0.2,\n",
    "                zoom_range=0.2,\n",
    "                horizontal_flip=True,\n",
    "                fill_mode='nearest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_datagen = ImageDataGenerator(rescale=1./255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_generator = train_datagen.flow_from_directory(\n",
    "                    train_dir,\n",
    "                    target_size=(150, 150),\n",
    "                    batch_size=20,\n",
    "                    class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "validation_generator = test_datagen.flow_from_directory(\n",
    "                        validation_dir,\n",
    "                        target_size=(150, 150),\n",
    "                        batch_size=20,\n",
    "                        class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='binary_crossentropy',\n",
    "                optimizer=optimizers.RMSprop(lr=2e-5),\n",
    "                metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "100/100 [==============================] - 101s 1s/step - loss: 0.6084 - acc: 0.6665 - val_loss: 0.4520 - val_acc: 0.8270\n",
      "Epoch 2/30\n",
      "100/100 [==============================] - 93s 926ms/step - loss: 0.4786 - acc: 0.8005 - val_loss: 0.3736 - val_acc: 0.8500\n",
      "Epoch 3/30\n",
      "100/100 [==============================] - 92s 925ms/step - loss: 0.4402 - acc: 0.7995 - val_loss: 0.3310 - val_acc: 0.8670\n",
      "Epoch 4/30\n",
      "100/100 [==============================] - 92s 924ms/step - loss: 0.4031 - acc: 0.8155 - val_loss: 0.3180 - val_acc: 0.8710\n",
      "Epoch 5/30\n",
      "100/100 [==============================] - 94s 936ms/step - loss: 0.3972 - acc: 0.8160 - val_loss: 0.3071 - val_acc: 0.8700\n",
      "Epoch 6/30\n",
      "100/100 [==============================] - 101s 1s/step - loss: 0.3778 - acc: 0.8325 - val_loss: 0.2815 - val_acc: 0.8770\n",
      "Epoch 7/30\n",
      "100/100 [==============================] - 101s 1s/step - loss: 0.3578 - acc: 0.8495 - val_loss: 0.2754 - val_acc: 0.8810\n",
      "Epoch 8/30\n",
      "100/100 [==============================] - 101s 1s/step - loss: 0.3528 - acc: 0.8465 - val_loss: 0.2690 - val_acc: 0.8920\n",
      "Epoch 9/30\n",
      "100/100 [==============================] - 101s 1s/step - loss: 0.3406 - acc: 0.8510 - val_loss: 0.2607 - val_acc: 0.8930\n",
      "Epoch 10/30\n",
      "100/100 [==============================] - 101s 1s/step - loss: 0.3431 - acc: 0.8490 - val_loss: 0.2587 - val_acc: 0.8910\n",
      "Epoch 11/30\n",
      "100/100 [==============================] - 101s 1s/step - loss: 0.3435 - acc: 0.8465 - val_loss: 0.2562 - val_acc: 0.8970\n",
      "Epoch 12/30\n",
      "100/100 [==============================] - 101s 1s/step - loss: 0.3246 - acc: 0.8625 - val_loss: 0.2555 - val_acc: 0.9010\n",
      "Epoch 13/30\n",
      "100/100 [==============================] - 101s 1s/step - loss: 0.3223 - acc: 0.8620 - val_loss: 0.2496 - val_acc: 0.9020\n",
      "Epoch 14/30\n",
      "100/100 [==============================] - 101s 1s/step - loss: 0.3150 - acc: 0.8610 - val_loss: 0.2506 - val_acc: 0.8960\n",
      "Epoch 15/30\n",
      "100/100 [==============================] - 101s 1s/step - loss: 0.3215 - acc: 0.8605 - val_loss: 0.2550 - val_acc: 0.8960\n",
      "Epoch 16/30\n",
      "100/100 [==============================] - 101s 1s/step - loss: 0.3076 - acc: 0.8715 - val_loss: 0.2535 - val_acc: 0.8920\n",
      "Epoch 17/30\n",
      "100/100 [==============================] - 101s 1s/step - loss: 0.3103 - acc: 0.8665 - val_loss: 0.2673 - val_acc: 0.8870\n",
      "Epoch 18/30\n",
      "100/100 [==============================] - 101s 1s/step - loss: 0.2969 - acc: 0.8760 - val_loss: 0.2416 - val_acc: 0.9070\n",
      "Epoch 19/30\n",
      "100/100 [==============================] - 101s 1s/step - loss: 0.3029 - acc: 0.8585 - val_loss: 0.2500 - val_acc: 0.8960\n",
      "Epoch 20/30\n",
      "100/100 [==============================] - 101s 1s/step - loss: 0.3094 - acc: 0.8640 - val_loss: 0.2417 - val_acc: 0.8970\n",
      "Epoch 21/30\n",
      "100/100 [==============================] - 101s 1s/step - loss: 0.2973 - acc: 0.8730 - val_loss: 0.2429 - val_acc: 0.8990\n",
      "Epoch 22/30\n",
      "100/100 [==============================] - 102s 1s/step - loss: 0.3077 - acc: 0.8675 - val_loss: 0.2379 - val_acc: 0.9030\n",
      "Epoch 23/30\n",
      "100/100 [==============================] - 102s 1s/step - loss: 0.2958 - acc: 0.8690 - val_loss: 0.2481 - val_acc: 0.8990\n",
      "Epoch 24/30\n",
      "100/100 [==============================] - 101s 1s/step - loss: 0.2897 - acc: 0.8740 - val_loss: 0.2391 - val_acc: 0.9000\n",
      "Epoch 25/30\n",
      "100/100 [==============================] - 101s 1s/step - loss: 0.2982 - acc: 0.8730 - val_loss: 0.2423 - val_acc: 0.9010\n",
      "Epoch 26/30\n",
      "100/100 [==============================] - 101s 1s/step - loss: 0.2841 - acc: 0.8800 - val_loss: 0.2371 - val_acc: 0.9050\n",
      "Epoch 27/30\n",
      "100/100 [==============================] - 101s 1s/step - loss: 0.2804 - acc: 0.8820 - val_loss: 0.2399 - val_acc: 0.9000\n",
      "Epoch 28/30\n",
      "100/100 [==============================] - 101s 1s/step - loss: 0.2851 - acc: 0.8820 - val_loss: 0.2475 - val_acc: 0.8960\n",
      "Epoch 29/30\n",
      "100/100 [==============================] - 102s 1s/step - loss: 0.2839 - acc: 0.8815 - val_loss: 0.2391 - val_acc: 0.9040\n",
      "Epoch 30/30\n",
      "100/100 [==============================] - 101s 1s/step - loss: 0.2801 - acc: 0.8785 - val_loss: 0.2372 - val_acc: 0.8990\n"
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(\n",
    "                train_generator,\n",
    "                steps_per_epoch=100,\n",
    "                epochs=30,\n",
    "                validation_data=validation_generator,\n",
    "                validation_steps=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FEATURE EXTRACTION WITH DATA AUGMENTATION and Fine-tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1 Add your custom network on top of an already-trained base network.\n",
    "\n",
    "2 Freeze the base network.\n",
    "\n",
    "3 Train the part you added.\n",
    "\n",
    "4 Unfreeze some layers in the base network.\n",
    "\n",
    "5 Jointly train both these layers and the part you added."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You already completed the first three steps when doing feature extraction. Let’s proceed with step 4: you’ll unfreeze your conv_base and then freeze individual layers\n",
    "inside it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"vgg16\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 150, 150, 3)]     0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 150, 150, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 150, 150, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 75, 75, 64)        0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 75, 75, 128)       73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 75, 75, 128)       147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 37, 37, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 37, 37, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 37, 37, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 37, 37, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 18, 18, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 18, 18, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 18, 18, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 18, 18, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 9, 9, 512)         0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 4, 4, 512)         0         \n",
      "=================================================================\n",
      "Total params: 14,714,688\n",
      "Trainable params: 0\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "conv_base.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(model.trainable_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_base.trainable = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(model.trainable_weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Freezing all layers up to a specific one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_trainable = False\n",
    "for layer in conv_base.layers:\n",
    "    if layer.name == 'block5_conv1':\n",
    "        set_trainable = True\n",
    "    if set_trainable:\n",
    "        layer.trainable = True\n",
    "    else:\n",
    "        layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(model.trainable_weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Fine-tuning the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='binary_crossentropy',\n",
    "optimizer=optimizers.RMSprop(lr=1e-5),\n",
    "metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "100/100 [==============================] - 94s 936ms/step - loss: 0.1364 - acc: 0.9435 - val_loss: 0.1861 - val_acc: 0.9280\n",
      "Epoch 2/10\n",
      "100/100 [==============================] - 94s 936ms/step - loss: 0.1211 - acc: 0.9505 - val_loss: 0.1888 - val_acc: 0.9290\n",
      "Epoch 3/10\n",
      "100/100 [==============================] - 94s 936ms/step - loss: 0.1122 - acc: 0.9570 - val_loss: 0.1777 - val_acc: 0.9350\n",
      "Epoch 4/10\n",
      "100/100 [==============================] - 102s 1s/step - loss: 0.1103 - acc: 0.9560 - val_loss: 0.1978 - val_acc: 0.9310\n",
      "Epoch 5/10\n",
      "100/100 [==============================] - 103s 1s/step - loss: 0.1130 - acc: 0.9575 - val_loss: 0.1767 - val_acc: 0.9360\n",
      "Epoch 6/10\n",
      "100/100 [==============================] - 102s 1s/step - loss: 0.1021 - acc: 0.9645 - val_loss: 0.2693 - val_acc: 0.9190\n",
      "Epoch 7/10\n",
      "100/100 [==============================] - 103s 1s/step - loss: 0.0993 - acc: 0.9615 - val_loss: 0.2289 - val_acc: 0.9250\n",
      "Epoch 8/10\n",
      "100/100 [==============================] - 103s 1s/step - loss: 0.0902 - acc: 0.9625 - val_loss: 0.1858 - val_acc: 0.9310\n",
      "Epoch 9/10\n",
      "100/100 [==============================] - 102s 1s/step - loss: 0.0905 - acc: 0.9630 - val_loss: 0.1861 - val_acc: 0.9340\n",
      "Epoch 10/10\n",
      "100/100 [==============================] - 102s 1s/step - loss: 0.0970 - acc: 0.9645 - val_loss: 0.1819 - val_acc: 0.9320\n"
     ]
    }
   ],
   "source": [
    "epochs = 10 # 100\n",
    "history = model.fit_generator(\n",
    "                train_generator,\n",
    "                steps_per_epoch=100,\n",
    "                epochs=epochs,\n",
    "                validation_data=validation_generator,\n",
    "                validation_steps=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving model\n",
    "model.save('cats_and_dogs_small_3.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "test_generator = test_datagen.flow_from_directory(\n",
    "                    test_dir,\n",
    "                    target_size=(150, 150),\n",
    "                    batch_size=20,\n",
    "                    class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test acc: 0.938\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate_generator(test_generator, steps=50)\n",
    "print('test acc:', test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "conda_gpu",
   "language": "python",
   "name": "conda_gpu"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
